.. .. image:: _static/goes2go_logo.png
.. This drop-shadow glow on the logo helps when in Darkmode
.. raw:: html
=======================
GOES-2-Go Documentation
=======================
GOES-2-Go is a python package that helps you download GOES-R series (GOES-East/16 and GOES-West/17/18) NetCDF files from the `Amazon Web Services `_ archive and provides RGB recipes for various RGB products.
.. toctree::
:maxdepth: 1
/user_guide/index
/reference_guide/index
Install
-------
The easiest way to install ``goes2go`` and its dependencies is with Conda from conda-forge.
.. code:: bash
conda install -c conda-forge goes2go
You may also create the provided Conda environment file `environment.yml
`_.
.. code:: bash
# Create the environment
conda env create -f environment.yml
# Update the environment
conda env update -f environment.yml
# Activate the environment
conda activate goes2go
Alternatively, ``goes2go`` is published on PyPI and you can install it with pip, but it requires some additional dependencies that you will have to install yourself:
- Python 3.8+
- Cartopy, which requires GEOS and Proj.
- MetPy
- Optional: Carpenter Workshop
When those are installed within your environment, then you can install GOES-2-go with pip.
.. code:: bash
# Latest published version
pip install goes2go
# ~~ or ~~
# Most recent changes
pip install git+https://github.com/blaylockbk/goes2go.git
Capabilities
------------
Download and Read Data
^^^^^^^^^^^^^^^^^^^^^^
First, create a GOES object to specify the satellite, data product, and domain you are interested in. The example below downloads the Multi-Channel Cloud Moisture Imagery for CONUS.
.. code-block:: python
from goes2go import GOES
# ABI Multi-Channel Cloud Moisture Imagry Product
G = GOES(satellite=16, product="ABI-L2-MCMIP", domain='C')
# Geostationary Lightning Mapper
G = GOES(satellite=17, product="GLM-L2-LCFA", domain='C')
# ABI Level 1b Data
G = GOES(satellite=17, product="ABI-L1b-Rad", domain='F')
.. note:: A complete listing of the products available are available at `here `_.
There are methods to do the following:
* List the available files for a time range
* Download data to your local drive for a specified time range
* Read the data into an xarray Dataset for a specific time
.. code-block:: python
# Produce a pandas DataFrame of the available files in a time range
df = G.df(start='2022-07-04 01:00', end='2022-07-04 01:30')
.. code-block:: python
# Download and read the data as an xarray Dataset nearest a specific time
ds = G.nearesttime('2022-01-01')
.. code-block:: python
# Download and read the latest data as an xarray Dataset
ds = G.latest()
.. code-block:: python
# Download data for a specified time range
G.timerange(start='2022-06-01 00:00', end='2022-06-01 01:00')
# Download recent data for a specific interval
G.timerange(recent='30min')
- `📖 Download latest `_
- `📖 Download nearest time `_
- `📖 Download time series `_
RGB Recipes for ABI
^^^^^^^^^^^^^^^^^^^
Generate RGB arrays for different RGB products. Check out the following notebook for a demonstration:
- `📖 RGB Recipes Docs `_
.. figure:: _static/TrueColor.png
:class: img-fluid
ABI TrueColor RGB image
Field of View
^^^^^^^^^^^^^
Advanced Baseline Imager (ABI)
""""""""""""""""""""""""""""""
GOES-West is centered over -137 W and GOES-East is centered over -75 W. When GOES was being tested, it was in a "central" position, outlined in the dashed black line. Below is the ABI field of view for the full disk:
- `📓 ABI field of view notebook `_
.. figure:: _static/ABI_field-of-view.png
:class: img-fluid
.. figure:: _static/ABI_field-of-view_16dom.png
:class: img-fluid
.. figure:: _static/ABI_field-of-view_17dom.png
:class: img-fluid
ABI full disk field of view
Geostationary Lightning Mapper (GLM)
""""""""""""""""""""""""""""""""""""
The GLM field of view is slightly smaller and limited by a bounding box. The field of view can be estimated.
- `📓 GLM field of view notebook `_
- `📓 More details on actual edges `_
.. figure:: _static/GLM_field-of-view.png
:class: img-fluid
Approximate GLM field of view
Useful Links
------------
- `👨🏻💻 Brian's GitHub notebooks `_
- `🎠 Beginner's Guide `_
- `📔 GOES-R Series Data Book `_
- `💻 Rammb Slider GOES Viewer `_
- `🐍 Unidata Plot GOES Data `_
- `🗺 Plotting tips form geonetcast blog `_
- `🐍 glmtools `_ For useful tools for GLM data.
- `⏲ Mesoscale Sector Historical Archive `_ shows maps of where mesoscale scans were at each time.
- `🗺 CIMSS Gridding GLM Tool `_
💾 Data Access
^^^^^^^^^^^^^^^^
Access GOES data through NOAA's Big Data Project partners
- `Amazon Web Services `_
- `🌐 Brian's Interactive GOES Download Page `_
- `🗃 AWS GOES-16 S3 Explorer `_
- `🗃 AWS GOES-17 S3 Explorer `_
- `Microsoft Azure `_ (Only GOES-16??)
- Google Cloud Platform (`GOES-16 `_ | `GOES-17 `_)
- `Download GOES data with rclone `_
Data Quality
^^^^^^^^^^^^^
- `⛑ GOES-17 ABI Data Quality (CIMSS) `_